Very accurate geo-location (geo-coding) of imagery taken at long range is a very large challenge.
Whereas GPS can supply a very accurate sensor position, the hardware for the required precision pointing
can have a very large cost. Roth, et al (2005) showed that because of the accuracy of lidar range-data, a
tri-lateration method (called Multi-Look Lidar or Multi-Look Geo-Coding) can achieve very accurate geocoding
at very long ranges and very low cost by using data-driven processing. This paper presents
extensive flight-testing results using commercial airborne lidar. Because the tri-lateration method produces
a large number of control points, the resulting accuracy of the geo-coded lidar data is somewhat better than
that predicted for a single control point due to control-point averaging.
Foliage penetration is a major application of airborne lidar systems. Typical ground resolutions
achieved for floodplain-mapping applications are of the order of meters. Much higher ground resolution
can be achieved by integrating multiple looks from several look-angles. This paper describes a new system
that can achieve very high ground sampling densities in forested environments at significant altitudes (6 kft) using a modified commercial lidar and a custom gimbal system. Absolute calibration of the gimbal
system demonstrated pointing knowledge comparable to the usual aircraft-fixed lidar performance (0.1-0.2
mrad). Bare-earth processing of the resultant data enables interactive virtual deforestation relative to a high-resolution ground.
The construction of 3D models from light detection and ranging (LIDAR) data requires reliable and accurate alignment
of multiple overlapping scans. While established manual and automated 3D alignment methods generally perform well,
aligning scans of complex scenes from arbitrary perspectives with small amounts of overlap remains challenging. The
projection information available with scanned LIDAR data is generally underutilized and may be better exploited to
simplify the alignment process, avoiding manually specified algorithm parameters and improving reliability. In this
work, we present projective methods for manual and automated 3D alignment and introduce a projective measure of
surface interpenetration to quantify alignment error. Performance is demonstrated with a combination of indoor and
outdoor scan sets, including cluttered forest scenes, and compared to results obtained using an established commercial
Precision geo-location (absolute accuracy of 20 cm or less) is required of high-resolution lidar data (1 m or less post spacing) for general surveying needs and high-quality visualization. Current open-loop airborne-hardware pointing-technology supports precision geo-location at short range (less than a few km). Precision geo-location at longer range can be achieved with more complex pointing-hardware but at substantial cost. This paper introduces the concept of multi-look lidar that has the potential for achieving long-range precision geo-location but at substantially reduced cost. In this concept, lidar data are collected at multiple look-angles that are consistent with Position-Dilution-Of-Precision (PDOP) requirements. The data are registered, triangulated, and block-adjusted with a dense set of self-generated control points. An analytic model is presented that shows that the error performance is independent of range. A flight test is described that validates the multi-look-lidar concept. Potential system-implementations are also described that can have minimal impact on hardware or conventional flight operations.
High-resolution (0.3-1 m) digital-elevation data is widely available from commercial sources. Whereas the production of two-dimensional (2D) mapping products from such data is standard practice, the visualization of such three-dimensional (3D) data has been problematic. The basis for this problem is the same as that for the large-model problem in computer graphics-- large amounts of geometry are difficult for current rendering algorithms and hardware. This paper describes a cost-effective solution to this problem that has two parts. First is the employment of the latest in cost-effective 3D chips and video boards that have recently emerged. The second part is the employment of quad-tree data structures for efficient data storage and retrieval during rendering. The result is the capability for real-time display of large (over tens of millions of samples) digital elevation models on modest PC-based systems. This paper shows several demonstrations of this approach using airborne lidar data. The implication of this work is a paradigm shift for geo-spatial information systems--3D data can now be as easy to use as 2D data.
A brief review is presented of neural network tools for Automatic Target Recognition (ATR) . These tools include collective computation for implementing a variety of computational-vision techniques learning and adaptation for pattern recognition knowledge integration for expert-system capabilities and beyondsupercomputer- level hardware. As a specific example neural networks for stereo vision are introduced as a potentially fruitful approach to ATR. Preliminary results are presented which show substantial performance improvements over previous stereo algorithms for producing accurate dense displacement maps. These maps can be used in turn to derive accurate geometrical shape information that can result in improved recognition performance. 1.
SC717: 3D Visualization Techniques for Laser Radar
Visualization of 3D-laser-radar data has special challenges beyond those for conventional computer graphics for 3D scanners. Such challenges include sampling limitations, very large data sets, obscuring material, increased noise, and the need for rapid identification of interesting features. The course provides an introduction to 3D-laser-radar characteristics relevant to visualization. Several key 3D processing and visualization methods (e.g., jitter noise reduction, data classification, surface generation, point-cloud display-enhancement, selectable transparency, and rapid display of large data sets) will be discussed.